Seed‐to‐seedling transitions exhibit distance‐dependent mortality but no strong spacing effects in a Neotropical forest
Bibliographic record
Abstract
Patterns of seed dispersal and seed mortality influence the spatial structure of plant communities and the local coexistence of competing species. Most seeds are dispersed in proximity to the parent tree, where mortality is also expected to be the highest, because of competition with siblings or the attraction of natural enemies. Whereas distance-dependent mortality in the seed-to-seedling transition was often observed in tropical forests, few studies have attempted to estimate the shape of the survival-distance curves, which determines whether the peak of seedling establishment occurs away from the parent tree (Janzen-Connell pattern) or if the peak attenuates but remains at the parent location (Hubbell pattern). In this study, we inferred the probability density of seed dispersal and two stages of seedling establishment (new recruits, and seedlings 20 cm or taller) with distance for 24 tree species present in the 50-ha Forest Dynamics Plot of Barro Colorado Island, Panama. Using data from seed traps, seedling survey quadrats, and tree-census records spanning the 1988-2014 period, we fit hierarchical Bayesian models including parameters for tree fecundity, the shape of the dispersal kernel, and overdispersion of seed or seedling counts. We combined predictions from multiple dispersal kernels to obtain more robust inferences. We find that Hubbell patterns are the most common and Janzen-Connell patterns are very rare among those species; that distance-dependent mortality may be stronger in the seed stage, in the early recruit stage, or comparable in both; and that species with larger seeds experience less overall mortality and less distance-dependent mortality. Finally, we describe how this modeling approach could be extended at a community scale to include less abundant species.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".